Abstract: This paper proposes a hierarchical depthwise graph convolutional neural network (HDGCN) for point cloud semantic segmentation. The main chanllenge for learning on point clouds is to capture ...

・畳み込み層の基礎的な知識を理解する ・全結合層と畳み込み層の差異について、説明できる ・畳み込み層の役割について説明できる ・畳み込み層のパラメータ数について理解する ・畳み込み層が適用できるデータの特性について理解する 直訳すると ...

In this section, I'd like to discuss potential issues with deep networks, specifically vanishing gradients. I will also be mentioning the possible solutions for this, residual connections and batch ...

Abstract: Depthwise separable convolution is useful for building small and lightweight networks. However, the hardware design of depthwise separable convolution unit has not been well studied. With an ...

thanks for the awesome work and for making it publicly available. I just have a question regarding the paper and the implementation of OACNNs. According to the paper the method was tested using ...

When comparing a 2D depthwise convolution implemented in PyTorch vs. pure JAX/XLA on GPU, I observed that the PyTorch version runs roughly 3× slower and draws substantially more power than the ...